Over the last 18 months I have learned many things about community engaged research, and yet, I know that I have only scratched the surface.  Through TCP, I had the opportunity to provide support to a local non-profit on an evaluation project.  I was excited about the opportunity to collaborate with a local organization through TCP, a program that I had heard much about when I first joined UC.  Plus the topic was quite interesting to me and relevant to my research agenda.  I was optimistic, and perhaps overly confident, in my ability to support this project.

There were several challenges from the start.  Most of these challenges had to do with the nature and availability of the data. In retrospect, many of these challenges would have likely been anticipated by a mid-career researcher with extensive experience in community-engaged work.  Instead, I made several assumptions about the condition and state of the data before seeing it that were based in my previous experience working with a similar large quantitative dataset. However, in that experience, I was working in a well-funded research lab where we had two dedicated data scientists that thoroughly cleaned and prepared all data before I received it.  The implications of this misjudgment were substantial and given the limited time and resources I had to put towards this work, it ended up taking much longer to complete than I would have liked.  This, no doubt, was an inconvenience for the community partner; I had given them a general idea of when I expected to have results ready, but that was before I had seen the data.  

In addition, data access limitations prevented us from being able to answer some of the research questions that we had intended to answer.  We had no way to get around this issue, but we made the best of it using the data that we did have available.  It became quite apparent that my multiple asks for additional data were also becoming burdensome for the community partner; they have limited data management and analysis capacity, which I had to be mindful of.  They also have to carefully manage their own partnership with a third organization who has exclusive “ownership” of and access to the data.  I understand that the politics of the situation, as well as the organization’s own internal constraints likely shaped the nature of their response to my data requests; nevertheless, the lack of certain data limited what types of analyses we were able to conduct.  Ultimately, we produced the most useful information that we could provide to address the organization’s expressed objectives of this evaluation.

While this project was not well suited for a class project, I was able to engage a graduate research assistant on it, which provided him with the opportunity to not only practice his quantitative data analysis skills, but also enabled him to be exposed to a topic that he was unfamiliar with and would likely never have been exposed to.  In this way, the experience was enriching for his professional development, and helped to inform his understanding about this important topic.  However, given that there were no instrumental resources attached to this project, I was only able to have a small portion of his time as he was committed to other projects. The role of a research assistant on a project like this, especially one with the right skillset, cannot be understated.

Despite this project being substantially hindered by several challenges, I am grateful for the opportunity to support the mission of TCP and the work of the partner agency on this project.  The most important lessons that I learned is to show myself grace: as an early career scholar who is committed to community-engaged research, I am slowly learning how to do this, but it takes time.  Also, if at all possible do NOT commit to a timeline or to answering specific questions without first seeing the data.  Community partners might think that they have all of the data needed to answer the questions that they are interested in or that they can access all needed data, but that may not be the case.  Or perhaps it is the case at one point, and once the work gets started circumstances could change thereby preventing data access.  These are just a few of the many lessons that I have learned from participating in this project.